Linear Kernel pattern matched discriminative deep convolutive neural network for dynamic web page ranking with big data

P. Sujai and V. Sangeetha

International Journal of Critical Infrastructures, 2024, vol. 20, issue 5, 416-434

Abstract: Websites and information are plentiful. Search engines return many pages based on user requests. Thus, unstructured web content compromises information retrieval. A new gestalt pattern matched linear kernel discriminant maxpooled deep convolutive neural network (GPMLKDMDCNN) is to rank web pages by query. At first, Szymkiewicz-Simpson coefficient and Gestalt pattern matching Paice-Husk method are to remove stop words and stem words during preparation. Fisher kernelised linear discriminant analysis then selects keywords from preprocessed data. Bivariate Rosenthal correlation is utilised for page rank-based correlation outcomes and saving time, and online sites are ranked by user query with higher accuracy. The experiment uses parameters such as accuracy, false-positive rate, ranking time, and memory consumption. The evaluation shows that the GPMLKDMDCNN method is superior in using the CACM dataset with maximum ranking accuracy of 5%, minimum false positive rate and memory consumption of 39% and 13%, and quicker ranking time by 20% than the existing methods, respectively.

Keywords: web pages ranking; maxpooled deep convolutive neural network; Szymkiewicz-Simpson coefficient; gestalt pattern matched Paice-Husk algorithm; Fisher Kernelised linear discriminant analysis; bivariate Rosenthal correlation. (search for similar items in EconPapers)
Date: 2024
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